Socioeconomic inequalities in molecular risk for chronic diseases observed in young adulthood.

Many common chronic diseases of aging are negatively associated with socioeconomic status (SES). This study examines whether inequalities can already be observed in the molecular underpinnings of such diseases in the 30s, before many of them become prevalent. Data come from the National Longitudinal Study of Adolescent to Adult Health (Add Health), a large, nationally representative sample of US subjects who were followed for over two decades beginning in adolescence. We now have transcriptomic data (mRNA-seq) from a random subset of 4,543 of these young adults. SES in the household-of-origin and in young adulthood were examined as covariates of a priori-defined mRNA-based disease signatures and of specific gene transcripts identified de novo. An SES composite from young adulthood predicted many disease signatures, as did income and subjective status. Analyses highlighted SES-based inequalities in immune, inflammatory, ribosomal, and metabolic pathways, several of which play central roles in senescence. Many genes are also involved in transcription, translation, and diverse signaling mechanisms. Average causal-mediated effect models suggest that body mass index plays a key role in accounting for these relationships. Overall, the results reveal inequalities in molecular risk factors for chronic diseases often decades before diagnoses and suggest future directions for social signal transduction models that trace how social circumstances regulate the human genome.

Socioeconomic Inequalities and Molecular Risk for Aging in Young Adulthood.

Diverse manifestations of biological aging often reflect disparities in socioeconomic status (SES). In this paper, we examine associations between indicators of SES and an mRNA-based aging signature during young adulthood, before clinical indications of aging are common. We use data from wave V (2016-2018) of the National Longitudinal Study of Adolescent to Adult Health, a nationally representative study of adults aged 33-43 years, with transcriptomic data from a subset of 2,491 participants. Biological aging is measured using 1) a composite transcriptomic aging signature previously identified by Peters et al.'s out-of-sample meta-analysis (Nat Commun. 2015;6:8570) and 2) 9 subsets that represent functional pathways of coexpressed genes. SES refers to income, education, occupation, subjective social status, and a composite measure combining these 4 dimensions. We examine hypothesized mechanisms through which SES could affect aging: body mass index, smoking, health insurance status, difficulty paying bills, and psychosocial stress. We find that SES-especially the composite measure and income-is associated with transcriptomic aging and immune, mitochondrial, ribosomal, lysosomal, and proteomal pathways. Counterfactual mediational models suggest that the mediators partially account for these associations. The results thus reveal that numerous biological pathways associated with aging are already linked to SES in young adulthood.

The Early Life Course of Body Weight and Gene Expression Signatures for Disease.

We examined the way body-weight patterns through the first 4 decades of life relate to gene expression signatures of common forms of morbidity, including cardiovascular disease (CVD), type 2 diabetes (T2D), and inflammation. As part of wave V of the nationally representative National Longitudinal Study of Adolescent to Adult Health (1997-2018) in the United States, mRNA abundance data were collected from peripheral blood (n = 1,132). We used a Bayesian modeling strategy to examine the relative associations between body size at 5 life stages-birth, adolescence, early adulthood, young adulthood, and adulthood-and gene expression-based disease signatures. We compared life-course models that consider critical or sensitive periods, as well as accumulation over the entire period. Our results are consistent with a sensitive-period model when examining CVD and T2D gene expression signatures: Birth weight has a prominent role for the CVD and T2D signatures (explaining 33.1% and 22.1%, respectively, of the total association accounted for by body size), while the most recent adult obesity status (ages 33-39) is important for both of these gene expression signatures (24.3% and 35.1%, respectively). Body size in all life stages was associated with inflammation, consistent with the accumulation model.

Disability pathways preceding death in England by socio-economic status.

The role of socio-economic status (SES) in the last years of life is an under-researched aspect of health inequalities. This study examines disability patterns preceding death using data from the English Longitudinal Study of Ageing. We use repeated measures latent class analysis to identify the most common pathways preceding death in terms of walking ability and limitations in activities of daily living. Three pathways emerge: one characterized by consistently low disability; a second by a constant high level of functional limitations; and a third by medium impairment. We examine how different SES indicators predict belonging to each disability pathway. Conditional on income, higher wealth is associated with a lower likelihood of belonging to the high disability pathway. Contrary to our expectations, we find no educational gradient in the pathways preceding death. Health inequalities in the last years of life seem to exist especially between individuals with different levels of wealth.

Do middle-aged and older people underreport loneliness? experimental evidence from the Netherlands.

Despite the growing acknowledgment of the importance of loneliness among older individuals, questionnaire length constraints may hinder the inclusion of common multi-item loneliness scales in surveys. Direct, single-item loneliness measures are a practical alternative, but scholars have expressed concerns that such measures may lead to underreporting. Our aim was to test whether such reservations are justified. We conducted a preregistered list experiment among 2,553 people aged 50 + who participated in the Dutch Longitudinal Internet studies for the Social Sciences (LISS) panel. The list experiment method has been developed to unobtrusively gather sensitive information. We compared the list experiment estimate of the prevalence of frequent loneliness with the corresponding direct question estimate to assess downward bias in the latter. Next to pooled models, we estimated models stratified by gender to assess whether loneliness underreporting differed between women and men. Relying on the direct question, we estimated that 5.9% of respondents frequently felt lonely. Our list experiment indicated that the prevalence of frequent loneliness was 13.1%. Although substantial in magnitude, the difference between both estimates was only marginally significant (Δb: 0.072, 95% CI: - 0.003;0.148, p = .06). No evidence of gender differences was found. Although we cannot be conclusive that loneliness estimates are biased downward when a direct question is used, our results call for caution with direct, single-item measures of loneliness if researchers want to avoid underreporting. Replications are needed to gain more precise insights into the extent to which direct, single-item loneliness measures are prone to downward reporting bias.

Socioeconomic status across the early life course predicts gene expression signatures of disease and senescence.

BACKGROUND: Socioeconomic status (SES) is associated with many chronic diseases, indicators of senescence and mortality. However, the changing salience of SES in the prediction of adult health is not well understood. Using mRNA-seq abundance data from wave V of the National Longitudinal Study of Adolescent to Adult Health (Add Health), we examine the extent to which SES across the early life course is related to gene expression-based signatures for chronic diseases, senescence and inflammation in the late 30s. METHODS: We use Bayesian methods to identify the most likely model of life course epidemiology (critical, sensitive and accumulation models) that characterises the changing importance of parental SES and SES during young (ages 27-30) and mid-adulthood (ages 36-39) in the prediction of the signatures. RESULTS: For most signatures, SES is an important predictor in all periods, although parental SES or SES during young adulthood are often the most predictive. For three signatures (components of diabetes, inflammation and ageing), critical period models involving the exclusive salience of SES in young adulthood (for diabetes) or parental SES (for inflammation and ageing) are most probable. The observed associations are likely mediated by body mass index. CONCLUSION: Models of life course patterns of SES may inform efforts to identify age-specific mechanisms by which SES is associated with health at different points in life and they also suggest an enhanced approach to prediction models that recognise the changing salience of risk factors.

A Bayesian functional approach to test models of life course epidemiology over continuous time.

BACKGROUND: Life course epidemiology examines associations between repeated measures of risk and health outcomes across different phases of life. Empirical research, however, is often based on discrete-time models that assume that sporadic measurement occasions fully capture underlying long-term continuous processes of risk. METHODS: We propose (i) the functional relevant life course model (fRLM), which treats repeated, discrete measures of risk as unobserved continuous processes, and (ii) a testing procedure to assign probabilities that the data correspond to conceptual models of life course epidemiology (critical period, sensitive period and accumulation models). The performance of the fRLM is evaluated with simulations, and the approach is illustrated with empirical applications relating body mass index (BMI) to mRNA-seq signatures of chronic kidney disease, inflammation and breast cancer. RESULTS: Simulations reveal that fRLM identifies the correct life course model with three to five repeated assessments of risk and 400 subjects. The empirical examples reveal that chronic kidney disease reflects a critical period process and inflammation and breast cancer likely reflect sensitive period mechanisms. CONCLUSIONS: The proposed fRLM treats repeated measures of risk as continuous processes and, under realistic data scenarios, the method provides accurate probabilities that the data correspond to commonly studied models of life course epidemiology. fRLM is implemented with publicly-available software.

Does Children's Education Improve Parental Health and Longevity? Causal Evidence from Great Britain.

Parents with better-educated children are healthier and live longer, but whether there is a causal effect of children's education on their parents' health and longevity is unclear. First, we demonstrate an association between adults' offspring education and parental mortality in the 1958 British birth cohort study, which remains substantial-about two additional years of life-even when comparing parents with similar socioeconomic status. Second, we use the 1972 educational reform in England and Wales, which increased the minimum school leaving age from 15 to 16 years, to identify the presence of a causal effect of children's education on parental health and longevity using census-linked data from the Office for National Statistics Longitudinal Study. Results reveal that children's education has no causal effects on a wide range of parental mortality and health outcomes. We interpret these findings discussing the role of universal health care and education for socioeconomic inequality in Great Britain.

A Bayesian approach to comparing common models of life-course epidemiology.

BACKGROUND: Life-course epidemiology studies people's health over long periods, treating repeated measures of their experiences (usually risk factors) as predictors or causes of subsequent morbidity and mortality. Three hypotheses or models often guide the analyst in assessing these sequential risks: the accumulation model (all measurement occasions are equally important for predicting the outcome), the critical period model (only one occasion is important) and the sensitive periods model (a catch-all model for any other pattern of temporal dependence). METHODS: We propose a Bayesian omnibus test of these three composite models, as well as post hoc decompositions that identify their best respective sub-models. We test the approach via simulations, before presenting an empirical example that relates five sequential measurements of body weight to an RNAseq measure of colorectal-cancer disposition. RESULTS: The approach correctly identifies the life-course model under which the data were simulated. Our empirical cohort study indicated with >90% probability that colorectal-cancer disposition reflected a sensitive process, with current weight being most important but prior body weight also playing a role. CONCLUSIONS: The Bayesian methods we present allow precise inferences about the probability of life-course models given the data and are applicable in realistic scenarios involving causal analysis and missing data.